Computer vision & CNNs: Cats vs. Dogs

Part 1: Data Preparation

Examine data:

cat("Cat images:", "\n")

Cat images: 

cat("total training cat images:", length(list.files(train_cats_dir)), "\n")

total training cat images: 1000 

cat("total validation cat images:", length(list.files(valid_cats_dir)), "\n")

total validation cat images: 500 

cat("total test cat images:", length(list.files(test_cats_dir)), "\n\n")

total test cat images: 500 

cat("Dog images:", "\n")

Dog images: 

cat("total training dog images:", length(list.files(train_dogs_dir)), "\n")

total training dog images: 1000 

cat("total validation dog images:", length(list.files(valid_dogs_dir)), "\n")

total validation dog images: 500 

cat("total test dog images:", length(list.files(test_dogs_dir)), "\n")

total test dog images: 500 

Part 2: Model 1

Define and compile model

• Four sequential conv and max pooling layers
• Flatten layer
• Densly-connected network
• Single binary output

model <- keras_model_sequential() %>%
  layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu", input_shape = c(150, 150, 3)) %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  
  layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = "relu") %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  
  layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  
  layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  
  layer_flatten() %>%
  
  layer_dense(units = 512, activation = "relu") %>%
  layer_dense(units = 1, activation = "sigmoid")

summary(model)

Compile the model:

model %>% compile(
  loss = "binary_crossentropy",
  optimizer = optimizer_rmsprop(lr = 1e-4),
  metrics = "accuracy"
)

Read images from directories

Use image_data_generator()

train_datagen <- image_data_generator(rescale = 1/255)
valid_datagen <- image_data_generator(rescale = 1/255)

train_generator <- flow_images_from_directory(
  train_dir,
  train_datagen,
  target_size = c(150, 150),
  batch_size = 20,
  class_mode = "binary"
)

validation_generator <- flow_images_from_directory(
  valid_dir,
  valid_datagen,
  target_size = c(150, 150),
  batch_size = 20,
  class_mode = "binary"
)

View batches

batch <- generator_next(train_generator)
str(batch)

List of 2
 $ : num [1:20, 1:150, 1:150, 1:3] 0.627 0.149 0.196 0.118 0.227 ...
 $ : num [1:20(1d)] 1 0 1 0 0 0 1 1 1 1 ...

Train the model

history <- model %>% fit_generator(
  train_generator,
  steps_per_epoch = 100,
  epochs = 30,
  validation_data = validation_generator,
  validation_steps = 50
)

View history

plot(history)

Save the model

model %>% save_model_hdf5("cats_and_dogs_small_1.h5")

Part 3: Data Augmentation

datagen <- image_data_generator(
  rescale = 1/255,
  rotation_range = 40,
  width_shift_range = 0.2,
  height_shift_range = 0.2,
  shear_range = 0.2,
  zoom_range = 0.2,
  horizontal_flip = TRUE,
  fill_mode = "nearest"
)

fnames <- list.files(train_cats_dir, full.names = TRUE)
img_path <- fnames[[1]]

img <- image_load(img_path, target_size = c(150, 150))
img_array <- image_to_array(img)
img_array <- array_reshape(img_array, c(1, 150, 150, 3))

augmentation_generator <- flow_images_from_data(
  img_array,
  generator = datagen,
  batch_size = 1
)

op <- par(mfrow = c(2, 2), pty = "s", mar = c(1, 0, 1, 0))
for (i in 1:4) {
  batch <- generator_next(augmentation_generator)
  plot(as.raster(batch[1,,,]))
}
par(op)

model <- keras_model_sequential() %>%
  layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu", input_shape = c(150, 150, 3)) %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = "relu") %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>% 
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_flatten() %>%
  layer_dense(units = 512, activation = "relu") %>%
  layer_dense(units = 1, activation = "sigmoid")

model %>% compile(
  loss = "binary_crossentropy",
  optimizer = optimizer_rmsprop(lr = 1e-4),
  metrics = "accuracy"
)

datagen <- image_data_generator(
  rescale = 1/255,
  rotation_range = 40,
  width_shift_range = 0.2,
  height_shift_range = 0.2,
  shear_range = 0.2,
  zoom_range = 0.2,
  horizontal_flip = TRUE,
)

test_datagen <- image_data_generator(rescale = 1/255)

train_generator <- flow_images_from_directory(
  train_dir,
  datagen,
  target_size = c(150, 150),
  batch_size = 32,
  class_mode = "binary"
)

validation_generator <- flow_images_from_directory(
  valid_dir,
  test_datagen,
  target_size = c(150, 150),
  batch_size = 32,
  class_mode = "binary"
)

history <- model %>%
  fit_generator(
    train_generator,
    steps_per_epoch = 100,
    epochs = 100,
    validation_data = validation_generator,
    validation_steps = 50
  )

2019-10-14 21:12:10.224374: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
Epoch 1/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.6897 - acc: 0.5371 - val_loss: 0.6823 - val_acc: 0.5590
Epoch 2/100

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100/100 [==============================] - 65s 654ms/step - loss: 0.6754 - acc: 0.5792 - val_loss: 0.6523 - val_acc: 0.6060
Epoch 3/100

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100/100 [==============================] - 65s 646ms/step - loss: 0.6452 - acc: 0.6121 - val_loss: 0.6233 - val_acc: 0.6478
Epoch 4/100

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100/100 [==============================] - 64s 640ms/step - loss: 0.6235 - acc: 0.6454 - val_loss: 0.6171 - val_acc: 0.6352
Epoch 5/100

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100/100 [==============================] - 64s 638ms/step - loss: 0.6141 - acc: 0.6610 - val_loss: 0.6103 - val_acc: 0.6783
Epoch 6/100

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100/100 [==============================] - 65s 647ms/step - loss: 0.6082 - acc: 0.6636 - val_loss: 0.5936 - val_acc: 0.6720
Epoch 7/100

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100/100 [==============================] - 64s 635ms/step - loss: 0.5861 - acc: 0.6831 - val_loss: 0.5935 - val_acc: 0.6732
Epoch 8/100

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100/100 [==============================] - 61s 611ms/step - loss: 0.5869 - acc: 0.6866 - val_loss: 0.5532 - val_acc: 0.7081
Epoch 9/100

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100/100 [==============================] - 62s 622ms/step - loss: 0.5720 - acc: 0.6913 - val_loss: 0.5561 - val_acc: 0.7081
Epoch 10/100

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100/100 [==============================] - 62s 617ms/step - loss: 0.5707 - acc: 0.7068 - val_loss: 0.5330 - val_acc: 0.7214
Epoch 11/100

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100/100 [==============================] - 62s 624ms/step - loss: 0.5524 - acc: 0.7172 - val_loss: 0.5298 - val_acc: 0.7234
Epoch 12/100

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100/100 [==============================] - 62s 624ms/step - loss: 0.5555 - acc: 0.7170 - val_loss: 0.6092 - val_acc: 0.6669
Epoch 13/100

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100/100 [==============================] - 62s 620ms/step - loss: 0.5574 - acc: 0.7142 - val_loss: 0.5084 - val_acc: 0.7437
Epoch 14/100

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100/100 [==============================] - 61s 609ms/step - loss: 0.5414 - acc: 0.7263 - val_loss: 0.5262 - val_acc: 0.7202
Epoch 15/100

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100/100 [==============================] - 61s 614ms/step - loss: 0.5396 - acc: 0.7289 - val_loss: 0.5077 - val_acc: 0.7303
Epoch 16/100

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100/100 [==============================] - 59s 592ms/step - loss: 0.5254 - acc: 0.7403 - val_loss: 0.5490 - val_acc: 0.7176
Epoch 17/100

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100/100 [==============================] - 58s 584ms/step - loss: 0.5332 - acc: 0.7320 - val_loss: 0.5467 - val_acc: 0.7157
Epoch 18/100

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100/100 [==============================] - 59s 590ms/step - loss: 0.5275 - acc: 0.7358 - val_loss: 0.5459 - val_acc: 0.7145
Epoch 19/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.5128 - acc: 0.7390 - val_loss: 0.4975 - val_acc: 0.7525
Epoch 20/100

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100/100 [==============================] - 59s 589ms/step - loss: 0.5128 - acc: 0.7399 - val_loss: 0.5031 - val_acc: 0.7481
Epoch 21/100

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100/100 [==============================] - 58s 584ms/step - loss: 0.5064 - acc: 0.7529 - val_loss: 0.4702 - val_acc: 0.7519
Epoch 22/100

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100/100 [==============================] - 59s 590ms/step - loss: 0.5042 - acc: 0.7541 - val_loss: 0.4921 - val_acc: 0.7532
Epoch 23/100

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100/100 [==============================] - 59s 587ms/step - loss: 0.5125 - acc: 0.7491 - val_loss: 0.4869 - val_acc: 0.7703
Epoch 24/100

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100/100 [==============================] - 58s 584ms/step - loss: 0.4982 - acc: 0.7541 - val_loss: 0.5421 - val_acc: 0.7354
Epoch 25/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.4892 - acc: 0.7626 - val_loss: 0.4813 - val_acc: 0.7773
Epoch 26/100

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100/100 [==============================] - 59s 591ms/step - loss: 0.4970 - acc: 0.7553 - val_loss: 0.4934 - val_acc: 0.7538
Epoch 27/100

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100/100 [==============================] - 59s 585ms/step - loss: 0.4828 - acc: 0.7699 - val_loss: 0.4481 - val_acc: 0.7900
Epoch 28/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.4792 - acc: 0.7666 - val_loss: 0.4977 - val_acc: 0.7563
Epoch 29/100

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100/100 [==============================] - 58s 584ms/step - loss: 0.4868 - acc: 0.7604 - val_loss: 0.4831 - val_acc: 0.7678
Epoch 30/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.4784 - acc: 0.7680 - val_loss: 0.4624 - val_acc: 0.7684
Epoch 31/100

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100/100 [==============================] - 59s 585ms/step - loss: 0.4726 - acc: 0.7721 - val_loss: 0.4592 - val_acc: 0.7849
Epoch 32/100

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100/100 [==============================] - 59s 587ms/step - loss: 0.4725 - acc: 0.7761 - val_loss: 0.4663 - val_acc: 0.7633
Epoch 33/100

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100/100 [==============================] - 58s 584ms/step - loss: 0.4554 - acc: 0.7842 - val_loss: 0.4824 - val_acc: 0.7766
Epoch 34/100

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100/100 [==============================] - 58s 582ms/step - loss: 0.4637 - acc: 0.7794 - val_loss: 0.4779 - val_acc: 0.7690
Epoch 35/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.4560 - acc: 0.7867 - val_loss: 0.4719 - val_acc: 0.7684
Epoch 36/100

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100/100 [==============================] - 58s 584ms/step - loss: 0.4579 - acc: 0.7790 - val_loss: 0.4185 - val_acc: 0.8109
Epoch 37/100

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100/100 [==============================] - 58s 583ms/step - loss: 0.4493 - acc: 0.7939 - val_loss: 0.5131 - val_acc: 0.7360
Epoch 38/100

  1/100 [..............................] - ETA: 52s - loss: 0.6311 - acc: 0.6875
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100/100 [==============================] - 58s 584ms/step - loss: 0.4514 - acc: 0.7879 - val_loss: 0.4694 - val_acc: 0.7766
Epoch 39/100

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100/100 [==============================] - 58s 584ms/step - loss: 0.4472 - acc: 0.7908 - val_loss: 0.5094 - val_acc: 0.7513
Epoch 40/100

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100/100 [==============================] - 59s 587ms/step - loss: 0.4527 - acc: 0.7877 - val_loss: 0.4977 - val_acc: 0.7589
Epoch 41/100

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100/100 [==============================] - 59s 585ms/step - loss: 0.4355 - acc: 0.7955 - val_loss: 0.4488 - val_acc: 0.7843
Epoch 42/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.4375 - acc: 0.8018 - val_loss: 0.4675 - val_acc: 0.7766
Epoch 43/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.4339 - acc: 0.8021 - val_loss: 0.5227 - val_acc: 0.7506
Epoch 44/100

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100/100 [==============================] - 59s 587ms/step - loss: 0.4331 - acc: 0.7977 - val_loss: 0.4564 - val_acc: 0.7874
Epoch 45/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.4287 - acc: 0.8050 - val_loss: 0.4905 - val_acc: 0.7690
Epoch 46/100

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100/100 [==============================] - 59s 590ms/step - loss: 0.4130 - acc: 0.8097 - val_loss: 0.5038 - val_acc: 0.7741
Epoch 47/100

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100/100 [==============================] - 59s 587ms/step - loss: 0.4199 - acc: 0.8049 - val_loss: 0.4381 - val_acc: 0.8046
Epoch 48/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.4178 - acc: 0.8056 - val_loss: 0.4417 - val_acc: 0.7881
Epoch 49/100

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100/100 [==============================] - 58s 584ms/step - loss: 0.4214 - acc: 0.8065 - val_loss: 0.4933 - val_acc: 0.7576
Epoch 50/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.4203 - acc: 0.8050 - val_loss: 0.4378 - val_acc: 0.8058
Epoch 51/100

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100/100 [==============================] - 58s 582ms/step - loss: 0.4148 - acc: 0.8068 - val_loss: 0.5920 - val_acc: 0.7227
Epoch 52/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.4022 - acc: 0.8182 - val_loss: 0.4666 - val_acc: 0.7868
Epoch 53/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.4025 - acc: 0.8131 - val_loss: 0.4581 - val_acc: 0.7849
Epoch 54/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.4044 - acc: 0.8128 - val_loss: 0.4465 - val_acc: 0.7931
Epoch 55/100

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100/100 [==============================] - 59s 589ms/step - loss: 0.4176 - acc: 0.8075 - val_loss: 0.4500 - val_acc: 0.7862
Epoch 56/100

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100/100 [==============================] - 58s 584ms/step - loss: 0.3987 - acc: 0.8179 - val_loss: 0.4672 - val_acc: 0.7874
Epoch 57/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.4013 - acc: 0.8197 - val_loss: 0.4574 - val_acc: 0.7963
Epoch 58/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.3843 - acc: 0.8289 - val_loss: 0.4381 - val_acc: 0.7874
Epoch 59/100

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100/100 [==============================] - 58s 583ms/step - loss: 0.3956 - acc: 0.8198 - val_loss: 0.4156 - val_acc: 0.8052
Epoch 60/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.3956 - acc: 0.8213 - val_loss: 0.4243 - val_acc: 0.7944
Epoch 61/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.3852 - acc: 0.8269 - val_loss: 0.4470 - val_acc: 0.7951
Epoch 62/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.3724 - acc: 0.8333 - val_loss: 0.4166 - val_acc: 0.8141
Epoch 63/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.3780 - acc: 0.8313 - val_loss: 0.4104 - val_acc: 0.8179
Epoch 64/100

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100/100 [==============================] - 59s 589ms/step - loss: 0.3885 - acc: 0.8185 - val_loss: 0.4163 - val_acc: 0.8071
Epoch 65/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.3786 - acc: 0.8270 - val_loss: 0.4171 - val_acc: 0.8211
Epoch 66/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.3597 - acc: 0.8348 - val_loss: 0.4201 - val_acc: 0.8128
Epoch 67/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.3631 - acc: 0.8390 - val_loss: 0.4594 - val_acc: 0.7906
Epoch 68/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.3686 - acc: 0.8299 - val_loss: 0.5076 - val_acc: 0.7563
Epoch 69/100

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100/100 [==============================] - 59s 587ms/step - loss: 0.3738 - acc: 0.8327 - val_loss: 0.4627 - val_acc: 0.7887
Epoch 70/100

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100/100 [==============================] - 59s 589ms/step - loss: 0.3733 - acc: 0.8323 - val_loss: 0.4307 - val_acc: 0.7995
Epoch 71/100

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100/100 [==============================] - 59s 592ms/step - loss: 0.3611 - acc: 0.8387 - val_loss: 0.4220 - val_acc: 0.8001
Epoch 72/100

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100/100 [==============================] - 59s 593ms/step - loss: 0.3640 - acc: 0.8398 - val_loss: 0.4041 - val_acc: 0.8274
Epoch 73/100

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100/100 [==============================] - 59s 587ms/step - loss: 0.3617 - acc: 0.8378 - val_loss: 0.4344 - val_acc: 0.7957
Epoch 74/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.3609 - acc: 0.8398 - val_loss: 0.4571 - val_acc: 0.8071
Epoch 75/100

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100/100 [==============================] - 59s 585ms/step - loss: 0.3553 - acc: 0.8387 - val_loss: 0.4392 - val_acc: 0.7951
Epoch 76/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.3594 - acc: 0.8398 - val_loss: 0.4435 - val_acc: 0.7881
Epoch 77/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.3465 - acc: 0.8501 - val_loss: 0.3785 - val_acc: 0.8325
Epoch 78/100

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100/100 [==============================] - 58s 585ms/step - loss: 0.3398 - acc: 0.8497 - val_loss: 0.4037 - val_acc: 0.8160
Epoch 79/100

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100/100 [==============================] - 59s 591ms/step - loss: 0.3247 - acc: 0.8584 - val_loss: 0.4256 - val_acc: 0.8198
Epoch 80/100

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100/100 [==============================] - 58s 583ms/step - loss: 0.3470 - acc: 0.8419 - val_loss: 0.4401 - val_acc: 0.7957
Epoch 81/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.3268 - acc: 0.8557 - val_loss: 0.5438 - val_acc: 0.7868
Epoch 82/100

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100/100 [==============================] - 59s 591ms/step - loss: 0.3491 - acc: 0.8467 - val_loss: 0.4573 - val_acc: 0.7951
Epoch 83/100

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100/100 [==============================] - 59s 586ms/step - loss: 0.3299 - acc: 0.8580 - val_loss: 0.4183 - val_acc: 0.8109
Epoch 84/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.3296 - acc: 0.8621 - val_loss: 0.4646 - val_acc: 0.7989
Epoch 85/100

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100/100 [==============================] - 59s 589ms/step - loss: 0.3258 - acc: 0.8533 - val_loss: 0.4359 - val_acc: 0.8154
Epoch 86/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.3349 - acc: 0.8460 - val_loss: 0.4300 - val_acc: 0.8255
Epoch 87/100

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100/100 [==============================] - 59s 592ms/step - loss: 0.3173 - acc: 0.8662 - val_loss: 0.4156 - val_acc: 0.8357
Epoch 88/100

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100/100 [==============================] - 59s 587ms/step - loss: 0.3187 - acc: 0.8602 - val_loss: 0.4079 - val_acc: 0.8306
Epoch 89/100

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100/100 [==============================] - 59s 590ms/step - loss: 0.3167 - acc: 0.8606 - val_loss: 0.3877 - val_acc: 0.8236
Epoch 90/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.3098 - acc: 0.8652 - val_loss: 0.4260 - val_acc: 0.8299
Epoch 91/100

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100/100 [==============================] - 59s 593ms/step - loss: 0.3099 - acc: 0.8662 - val_loss: 0.4725 - val_acc: 0.8065
Epoch 92/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.3172 - acc: 0.8551 - val_loss: 0.4116 - val_acc: 0.8160
Epoch 93/100

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100/100 [==============================] - 59s 588ms/step - loss: 0.3177 - acc: 0.8637 - val_loss: 0.4575 - val_acc: 0.8077
Epoch 94/100

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100/100 [==============================] - 58s 585ms/step - loss: 0.3140 - acc: 0.8604 - val_loss: 0.5006 - val_acc: 0.7830
Epoch 95/100

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100/100 [==============================] - 59s 590ms/step - loss: 0.2920 - acc: 0.8628 - val_loss: 0.4254 - val_acc: 0.8204
Epoch 96/100

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100/100 [==============================] - 59s 587ms/step - loss: 0.2997 - acc: 0.8715 - val_loss: 0.4760 - val_acc: 0.7938
Epoch 97/100

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100/100 [==============================] - 57s 572ms/step - loss: 0.3112 - acc: 0.8624 - val_loss: 0.4667 - val_acc: 0.8154
Epoch 98/100

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100/100 [==============================] - 59s 585ms/step - loss: 0.2891 - acc: 0.8823 - val_loss: 0.4392 - val_acc: 0.8211
Epoch 99/100

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 88/100 [=========================>....] - ETA: 6s - loss: 0.3199 - acc: 0.8521
 89/100 [=========================>....] - ETA: 5s - loss: 0.3184 - acc: 0.8528
 90/100 [==========================>...] - ETA: 5s - loss: 0.3182 - acc: 0.8523
 91/100 [==========================>...] - ETA: 4s - loss: 0.3171 - acc: 0.8532
 92/100 [==========================>...] - ETA: 4s - loss: 0.3173 - acc: 0.8531
 93/100 [==========================>...] - ETA: 3s - loss: 0.3168 - acc: 0.8537
 94/100 [===========================>..] - ETA: 3s - loss: 0.3164 - acc: 0.8536
 95/100 [===========================>..] - ETA: 2s - loss: 0.3171 - acc: 0.8532
 96/100 [===========================>..] - ETA: 2s - loss: 0.3166 - acc: 0.8531
 97/100 [============================>.] - ETA: 1s - loss: 0.3164 - acc: 0.8540
 98/100 [============================>.] - ETA: 1s - loss: 0.3186 - acc: 0.8535
 99/100 [============================>.] - ETA: 0s - loss: 0.3195 - acc: 0.8531
100/100 [==============================] - 59s 592ms/step - loss: 0.3183 - acc: 0.8536 - val_loss: 0.4283 - val_acc: 0.8211
Epoch 100/100

  1/100 [..............................] - ETA: 53s - loss: 0.1721 - acc: 0.9688
  2/100 [..............................] - ETA: 52s - loss: 0.1918 - acc: 0.9375
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 10/100 [==>...........................] - ETA: 48s - loss: 0.2490 - acc: 0.9000
 11/100 [==>...........................] - ETA: 46s - loss: 0.2657 - acc: 0.8958
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 80/100 [=======================>......] - ETA: 10s - loss: 0.2889 - acc: 0.8722
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 82/100 [=======================>......] - ETA: 9s - loss: 0.2893 - acc: 0.8723
 83/100 [=======================>......] - ETA: 8s - loss: 0.2884 - acc: 0.8720
 84/100 [========================>.....] - ETA: 8s - loss: 0.2867 - acc: 0.8728
 85/100 [========================>.....] - ETA: 7s - loss: 0.2856 - acc: 0.8735
 86/100 [========================>.....] - ETA: 7s - loss: 0.2869 - acc: 0.8728
 87/100 [=========================>....] - ETA: 6s - loss: 0.2876 - acc: 0.8728
 88/100 [=========================>....] - ETA: 6s - loss: 0.2889 - acc: 0.8725
 89/100 [=========================>....] - ETA: 5s - loss: 0.2890 - acc: 0.8722
 90/100 [==========================>...] - ETA: 5s - loss: 0.2898 - acc: 0.8719
 91/100 [==========================>...] - ETA: 4s - loss: 0.2896 - acc: 0.8722
 92/100 [==========================>...] - ETA: 4s - loss: 0.2907 - acc: 0.8716
 93/100 [==========================>...] - ETA: 3s - loss: 0.2900 - acc: 0.8723
 94/100 [===========================>..] - ETA: 3s - loss: 0.2898 - acc: 0.8720
 95/100 [===========================>..] - ETA: 2s - loss: 0.2883 - acc: 0.8730
 96/100 [===========================>..] - ETA: 2s - loss: 0.2888 - acc: 0.8724
 97/100 [============================>.] - ETA: 1s - loss: 0.2919 - acc: 0.8708
 98/100 [============================>.] - ETA: 1s - loss: 0.2933 - acc: 0.8705
 99/100 [============================>.] - ETA: 0s - loss: 0.2923 - acc: 0.8712
100/100 [==============================] - 59s 588ms/step - loss: 0.2936 - acc: 0.8709 - val_loss: 0.4497 - val_acc: 0.8046

model %>% save_model_hdf5("cats_and_dogs_small_2.h5")